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Found 3 papers, 1 papers with code
Towards real-time object recognition and pose estimation in point clouds
Our proposal achieves, in the best case, an accuracy performance of almost 83\% on the RGB-D Scenes dataset.
Learning to Orient Surfaces by Self-supervised Spherical CNNs
In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds.
Boosting Object Recognition in Point Clouds by Saliency Detection
Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications.
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